Instructions to use prithivMLmods/Muscae-Qwen3-UI-Code-4B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use prithivMLmods/Muscae-Qwen3-UI-Code-4B with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="prithivMLmods/Muscae-Qwen3-UI-Code-4B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("prithivMLmods/Muscae-Qwen3-UI-Code-4B")
model = AutoModelForCausalLM.from_pretrained("prithivMLmods/Muscae-Qwen3-UI-Code-4B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use prithivMLmods/Muscae-Qwen3-UI-Code-4B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "prithivMLmods/Muscae-Qwen3-UI-Code-4B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "prithivMLmods/Muscae-Qwen3-UI-Code-4B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/prithivMLmods/Muscae-Qwen3-UI-Code-4B

SGLang

How to use prithivMLmods/Muscae-Qwen3-UI-Code-4B with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "prithivMLmods/Muscae-Qwen3-UI-Code-4B" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "prithivMLmods/Muscae-Qwen3-UI-Code-4B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "prithivMLmods/Muscae-Qwen3-UI-Code-4B" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "prithivMLmods/Muscae-Qwen3-UI-Code-4B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use prithivMLmods/Muscae-Qwen3-UI-Code-4B with Docker Model Runner:
```
docker model run hf.co/prithivMLmods/Muscae-Qwen3-UI-Code-4B
```

prithivMLmods commited on Sep 24, 2025

Commit

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@@ -2,6 +2,8 @@
 license: apache-2.0
 ---
 # **Muscae-Qwen3-UI-Code-4B**
 > **Muscae-Qwen3-UI-Code-4B** is a reasoning-enhanced model fine-tuned on **Qwen** using the **GPT-OSS Web UI Coding dataset traces**, specializing in **web interface coding**, **structured generation**, and **polished token probabilities**.
@@ -31,7 +33,6 @@ license: apache-2.0
 6. **Optimized Lightweight Footprint**
    Compact **4B parameter size**, deployable on **mid-range GPUs**, **developer workstations**, and **edge build servers** while maintaining high-quality UI generation.
----
 ## **Quickstart with Transformers**

 license: apache-2.0
 ---
+![2](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/P-LIpWdt5ypMGIfE5kJiK.png)
 # **Muscae-Qwen3-UI-Code-4B**
 > **Muscae-Qwen3-UI-Code-4B** is a reasoning-enhanced model fine-tuned on **Qwen** using the **GPT-OSS Web UI Coding dataset traces**, specializing in **web interface coding**, **structured generation**, and **polished token probabilities**.
 6. **Optimized Lightweight Footprint**
    Compact **4B parameter size**, deployable on **mid-range GPUs**, **developer workstations**, and **edge build servers** while maintaining high-quality UI generation.
 ## **Quickstart with Transformers**